Temporal Event Sequence Simplification

Monroe, Megan; Lan, Rongjian; Lee, Han-Seung; Plaisant, Catherine; Shneiderman, Ben

doi:10.1109/tvcg.2013.200

Cited by 230 publications

(177 citation statements)

References 20 publications

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“…Given our problem, we discuss the second category. These visualizations focus on visualizing data from a group of patients in order to assist clinical researchers and quality assurance analysts in assessing and improving the quality of care of patients [13,15,19,33,36].…”

Section: Related Workmentioning

confidence: 99%

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Loorak

Périn

Kamal

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan. Permanent repository link

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Section: Related Workmentioning

confidence: 99%

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Loorak

Périn

Kamal

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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“…LifeFlow and EventFlow (Monroe et al 2013) are tools for event sequence analytics for a group of patients. They extract and highlight the common event sequences from patient records.…”

Section: Related Workmentioning

confidence: 99%

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

Zhao

Parvinzamir

Wilson

et al. 2016

J Vis

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Patient empowerment delivers health and social care services that enable people to gain more control of their healthcare needs. With the advancement of sensor technologies, it is increasingly possible to monitor people's health with dedicated wearable sensors. The consistent measurements from a variety of wearable sensors imply that a huge amount of data may be exploited to monitor and predict people's health using medically proven models. In the process of health data representation and analysis, visualisation can be employed to promote data analysis and knowledge discovery via mature visual paradigms and welldesigned user interactions. In this paper, we introduce the role of visualisation for individualised health monitoring and risk management in the background of a European Commission funded project, which aims to provide self-management of cardiorenal diseases with the assistance of wearable sensors. The DOI 10.1007/s12650-016-0402-6 visualisation components of health monitoring, risk model exploration, and risk analysis are presented to achieve personalised health and risk monitoring and to promote people's wellbeing. It allows the patients not only to view existing risks, but also to gain awareness of the right pathway to change their lifestyles in order to reduce potential health risks.

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“…LifeFlow [25] and EventFlow [16] are tools for event sequence analytics for a group of patients. They extract and highlight the common event sequence from patient records.…”

Section: Related Workmentioning

confidence: 99%

Visual Analytics for Health Monitoring and Risk Management in CARRE

Zhao

Parvinzamir

Wei

et al. 2016

E-Learning and Games

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Abstract. With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people's health in such a consistent way that the sensors may become people's lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people's health.

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Temporal Event Sequence Simplification

Cited by 230 publications

References 20 publications

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

Visual Analytics for Health Monitoring and Risk Management in CARRE

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